E01 | Macro Paper Warehouse

Disaggregated Economic Accounts

Mon, 01 Jan 0001 00:00:00 +0000

This paper develops and implements a system of disaggregated economic accounts that breaks down national accounting positions into bilateral flows between small groups of consumers, producers, the government, and the rest of the world. Standard national accounts document aggregate income and production plus input-output trade between producer industries; they contain no comprehensive data on which consumers buy from which producers or which producers pay income to which consumers. The paper fills this gap by measuring, for Denmark, all 36 positions in the UN System of National Accounts (SNA) — consumer spending, labor compensation, profit income, intermediates trade, government transfers and taxes, and foreign trade — as bilateral cell-to-cell flows, satisfying all national accounting identities at the level of individual cells and at the aggregate level. The data reveal systematic stylized facts about domestic spending shares, gravity of spending, urban bias, and assortative matching between consumer and producer characteristics. Combining the disaggregated accounts with a general equilibrium model with nominal wage rigidities, the paper shows that fiscal transfer multipliers vary substantially across consumer cells — from below 1 to above 2 — depending on the spending intensity of recipient cells on the slack (unemployed) portion of the economy. Applying the framework to a hypothetical U.S. tariff shock on Denmark (calibrated to July 2025 effective tariff levels on China), the paper demonstrates that the cells generating the highest multipliers are not those directly exposed to the shock or even those made slack, but those whose spending intensity on slack cells is high. The disaggregated accounts allow the government to select more effective fiscal policies: choosing transfers targeting high-spending-intensity cells saves approximately 0.4–0.7% of Danish GDP relative to programs targeting low-intensity cells, for the same GDP stimulus.

Measurement framework (Section II): The paper assigns every Danish adult to one of approximately 2,744 consumer cells, defined by the interaction of 98 municipalities (regions) and 28 industries (industry of main employment). Every production establishment is assigned to one of approximately 2,646 producer cells by region and industry. Median consumer cell contains 658 adults; median producer cell contains 47 establishments. The circular flow includes: (i) consumer spending on domestic and foreign producers; (ii) labor compensation paid by producer cells to consumer cells; (iii) profit income (dividends, mixed income, owner-occupied housing surplus) from producers to consumers; (iv) intermediates trade between domestic producers; (v) foreign trade; (vi) government taxes, transfers, and spending. A “bottom-up” approach uses microdata — geocoded transaction records from Danske Bank (largest Danish bank) and administrative government registers — to directly measure bilateral flows; a “top-down” approach distributes aggregate flows using assignment algorithms. Year: 2018. Data available at disaggregatedaccounts.com.

Stylized facts (Section IV):

1. Domestic spending shares (§IV.B): The share of a consumer cell’s spending going to domestic rather than foreign producers ranges from 75% to almost 100% (average 92%). Rural (small-population) cells, older cells, and less college-educated cells have higher domestic spending shares. Population size, average age, and college share jointly explain about half of the cross-cell variation in domestic shares; the patterns hold within industry and within region. The majority of foreign spending goes to travel-related and specialized retail categories (hotels, airlines, food away from home, clothing).

2. Gravity (§IV.C): Consumer spending declines with distance (log-log gradient = −1.33, column 1 of Table II). On average, roughly 50% of spending stays in the home region and an additional 10% goes to regions within 25 km. The distance gradient is steeper for groceries and fuel (local, in-person purchases) and shallower for telecommunications, insurance, and hotels. Rural, older, and less college-educated consumers spend more locally (stronger distance gradient, consistent with higher domestic shares).

3. Urban bias (§IV.E): Consumer spending flows disproportionately toward large cities. The 15 largest regions receive 34% of national consumer spending while accounting for only 27% of consumers. Urban bias is absent for everyday purchases (groceries) and strong for irregular or remote purchases (telecommunications, specialized retail). Rural consumers also visit urban regions in person, so urban bias is present in card payments too.

4. Assortative spending (§IV.D): Consumers tend to spend on producer cells employing workers with similar characteristics. Age of consumers and average age of workers in receiving cells are positively correlated (β = 0.178); college share similarly (β = 0.120); domestic spending share similarly (β = 0.203). The slopes are well below 1 (consumers purchase from many cells), but mild assortative spending reinforces first-order domestic spending patterns through higher-order connections.

5. Triangular flows (§IV.F): A distinctive cross-regional pattern: consumer spending and intermediates trade flow on net from rural to urban regions (urban regions run a net internal trade surplus); rural regions run a net external surplus (rural manufacturers export; e.g., Novo Nordisk in Kalundborg, Vestas in Nakskov); urban regions import relatively more from abroad. This triangular flow arises from urban consumption amenities and urban business service concentration.

Spending intensity (§IV.G): The paper constructs a reduced-form measure capturing, for each consumer cell i, how much its spending contributes to the income of a target group of cells — accounting for all higher-order connections (the infinite sum over indirect spending chains). The domestic spending intensity of cell i is defined recursively as the sum over all domestic producer cells j of (spending share αji × domestic spending intensity of producer cell j). Values range from roughly 0.4 to 0.9. The measure is strictly greater than the direct domestic spending share because the recursive formula incorporates second- and higher-order domestic connections. Domestic spending intensity is higher for rural, older, and less college-educated cells (consistent with the stylized facts). A spending intensity on slack cells can be constructed in the same way by replacing the target group with cells experiencing demand-driven unemployment.

General equilibrium model (Sections V–VI): The model is a static small open economy with many consumer and producer cells. Consumer utility is Cobb-Douglas over goods from all producer cells and foreign goods. Each producer cell’s production function is Cobb-Douglas with decreasing returns to scale (equivalent to a fixed factor). The key friction is downward nominal wage rigidity: Wi ≥ (1−δ)W̄i. When demand for a cell’s labor falls sufficiently (more than fraction δ), the wage rigidity binds and some workers in that cell become slack (unemployed demand-determined). A fiscal transfer to consumer cell i raises its income, which stimulates spending, which flows through the disaggregated network to raise labor demand across cells. The multiplier is higher when recipient spending flows disproportionately to slack cells, generating additional employment. The model is calibrated using the measured disaggregated accounts: spending shares αji, profit shares κij, labor shares λij, intermediates shares ωjj′, and tax rates are all taken directly from the disaggregated data. Baseline elasticity of substitution = 1 (Cobb-Douglas); robustness checks use short-run elasticities (< 1) and long-run elasticities (> 1), with no material change in conclusions.

Analytical result (Proposition 1): In an economy-wide recession (all cells slack), the vector of transfer multipliers is µ = ϕ′ · (I − M)⁻¹ · M · D((1 − τ̄ᵢ)⁻¹), where M is a transformed Leontief-style spending matrix incorporating the disaggregated accounts and τ̄ᵢ are fiscal externalities. The key insight is that the multiplier of cell i’s transfer is closely linked to its spending intensity on all other domestic cells, with all higher-order connections captured by the (I − M)⁻¹ M term. A cell’s multiplier is high when: (i) it spends domestically rather than on imports; (ii) it spends on producers that in turn employ domestic workers in slack cells; and (iii) these higher-order effects amplify through the circular flow.

Economy-wide recession: quantitative multipliers (Table III):

Transfer policy	Multiplier	Cost to raise GDP by 5% (bn DKK)
Uniform (all adults)	1.04	96.08
Top 10% domestic spending intensity	1.21	81.99
2018 child tax credit	1.02	97.85
2022 inflation relief to elderly	1.13	88.11
2023 housing rent inflation support	1.03	96.45
Construction worker support	1.23	81.16
Consulting/IT worker support	0.95	105.22

High-multiplier policies (construction workers, 2022 elderly relief) target rural, older, less college-educated cells with high domestic spending intensity. Low-multiplier policies (consulting/IT workers, 2023 housing relief, 2018 child tax credit) target urban, young, or college-educated cells with lower domestic intensity. The gap between the best and worst policies amounts to savings of roughly 15 bn DKK (≈ 2.4 bn USD), or 0.4–0.7% of Danish GDP, for the same aggregate GDP impact.

U.S. tariff shock application (Section VII): The paper analyzes a hypothetical U.S. tariff increase to 41.4% (the July 2025 effective U.S. tariff on China) on Danish exports, motivated by Greenland tensions. The shock reduces export revenue by 41.4% for each producer cell, with direct exposure varying by region: Billund (Lego headquarters), Kalundborg (pharmaceuticals), and a Copenhagen manufacturing hinterland face the largest direct declines — up to 8% of total regional sales. The shock propagates through the disaggregated network; cells whose income falls by more than 4% become slack. Key findings:

Regional slackness follows direct exposure but is also shaped by proximity to other exposed regions (urban bias propagates the shock to cities) and isolation (Billund has high direct exposure but low slackness relative to exposure because it is geographically isolated from other high-exposure cells)
Transfer multipliers for this heterogeneous recession (Proposition 2) depend on spending intensity on slack cells, not on direct exposure or own slackness
Table IV (R² for multiplier): slack cell indicator alone explains R² = 0.015; direct spending share on slack raises R² to 0.366; spending intensity on slack cells raises R² to 0.769 (column 3); adding both spending share and spending intensity on slack reaches R² = 0.840 (column 4)
Billund, despite high exposure, has low multiplier because its spending (often local to a low-exposure vicinity) does not create labor demand for slack cells elsewhere
Some of the highest-multiplier regions are themselves non-slack but are surrounded by many slack cells, so their spending effectively employs slack workers

Dynamic model (Section VIII): The paper extends to a dynamic OLG (Blanchard-Yaari) model with heterogeneous marginal propensities to consume (MPCs) calibrated from a 2009 Danish fiscal policy. Key result: static and year-4 dynamic multipliers are closely correlated (slope ≈ 0.898). Long-run cumulative multipliers exactly equal static multipliers (formally proved in Appendix V.F): in the long run, all transfers are fully spent. MPCs and domestic spending intensity are complementary determinants of dynamic multipliers — targeting high-MPC cells amplifies short-horizon (year 0–2) multipliers, while targeting high-spending-intensity cells shapes both short- and long-run multipliers. The paper’s main mechanism (spending intensity on slack cells) is robust at all horizons.

Robustness (Section IX): (i) Counterfactual accounts with reversed stylized patterns (e.g., rural cells spending like urban cells) lead to substantially different multipliers — the specific measured patterns drive the results. (ii) Imposing standard simplifying assumptions (consumer spending flows only to local producers; spending flows across regions in proportion to intermediate trade) misses most of the multiplier variation. (iii) The mechanism is similarly important in less open economies. (iv) Low short-run and high long-run substitution elasticities (from the trade literature) produce similar multiplier rankings across cells.

Scope conditions: The implementation is a proof of concept for Denmark, using existing micro data from a single large bank and government registers; full coverage of all banks and complete data on within-firm flows would strengthen measurement. Capital-related transactions (saving, investment, financial assets) are aggregated into a single capital accumulation cell — disaggregating these would require different data. The model is intentionally static (with a dynamic extension), abstracting from price adjustment dynamics beyond the NK wage rigidity. The analysis is a partial equilibrium in the sense that monetary policy response is not modeled; the fixed exchange rate assumption is realistic for Denmark (pegged to the Euro) but may not transfer to economies with flexible rates. The proof of concept suggests that national statistical agencies could benefit substantially from measuring disaggregated flows through refined surveys.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is missing from standard national accounts that this paper’s system provides?

Standard national accounts measure aggregate consumer spending, income, and output, plus intermediates trade among producer industries (input-output tables); what they do not measure is which specific consumer groups buy from which specific producer groups, or which specific producer groups pay labor and profit income to which specific consumer groups. This means that propagation of a shock through the circular flow — e.g., a tariff shock that reduces exports by rural manufacturers, which reduces income for rural workers, who then reduce spending on urban services, which reduces urban workers’ income — cannot be traced without simplifying assumptions (like “spending flows only to local producers”) that the disaggregated data shows to be empirically inaccurate. The paper provides a proof of concept demonstrating that measuring these bilateral consumer-to-producer and producer-to-consumer flows, while satisfying all national accounting identities, is feasible with existing micro data and yields policy-relevant variation in fiscal multipliers.

Q2. Why do rural, older, and less college-educated consumer cells have higher fiscal multipliers during an economy-wide recession?

These groups have higher domestic spending intensity — a higher fraction of their spending reaches domestic consumers rather than leaking abroad — because they spend less on international tourism, less on imported goods accessed through online retail or urban services, and more on local goods purchased in person. The gravity patterns (stronger distance gradient) and direct domestic spending shares document this directly: rural consumers allocate ~92–100% of spending to domestic producers versus ~75–80% for urban young college-educated consumers. When all cells are slack, a transfer to a high-domestic-intensity cell circulates more within the country, generating more rounds of domestic income and employment before leaking to imports. The mild assortative spending pattern further reinforces the first-order effect: spending by rural older consumers flows toward producer cells employing workers with similar characteristics, who also spend domestically, so higher-order connections amplify rather than dilute the domestic spending effect.

Q3. Why does targeting directly exposed or slack cells not guarantee a high transfer multiplier after the U.S. tariff shock?

A transfer raises GDP by increasing spending, which creates labor demand for other consumer cells; a transfer to a slack cell only generates a high multiplier if that cell’s spending flows toward other slack cells (directly or through indirect chains) — not if it flows toward non-slack cells or abroad. The tariff shock creates isolated pockets of slackness in rural manufacturing regions (e.g., Billund for Lego) that are geographically far from other slack regions; Billund consumers spend locally (gravity) and their locality is not itself a center of other slack cells. In contrast, regions near Copenhagen with moderate direct exposure may have high multipliers if they are close to many other slack manufacturing cells — their spending generates employment across the slack network. The R² decomposition confirms this: knowing a cell is slack explains only 1.5% of multiplier variation (R² = 0.015), while knowing its spending intensity on slack cells explains 76.9% (R² = 0.769).

Q4. How does the paper ensure that the disaggregated flows satisfy national accounting identities?

The system is designed so that every cell’s total inflows equal total outflows (a cell-level balance sheet constraint), and the sum of all cell-level flows equals the corresponding national aggregate from the SNA — both conditions are imposed by construction, not just approximated. For most positions, a bottom-up approach uses observed bilateral microdata (e.g., card payments from Danske Bank directly measure consumer spending by consumer cell i at producer cell j); for positions without direct microdata, a top-down algorithm distributes an aggregate total across cells using assignment rules grounded in the microdata. This dual approach ensures national comprehensiveness (the sum of disaggregated flows equals aggregate national accounts) and individual consistency (cell-level identities hold), unlike existing regional accounts or social accounting matrices that satisfy only one of these constraints.

Q5. What is the relationship between spending intensity and the standard fiscal multiplier formula?

The cell-level multiplier (dGDP/dTi) in Proposition 1 equals approximately the cell’s spending intensity on domestic cells, corrected for fiscal externalities and price effects of the fixed factor. The formal difference is that the model multiplier involves the matrix (I − M)⁻¹M where M incorporates both spending and production shares (through which price changes for the fixed factor enter), while the reduced-form spending intensity uses only the spending matrix. Despite this difference, the two measures are highly correlated empirically: the regression of cell-level multipliers on domestic spending intensity has a slope of approximately 1.66 for static multipliers. The spending intensity can thus be calculated directly from the disaggregated accounts without solving the full general equilibrium model, making it a practical statistic for policy guidance.

Q6. How does the dynamic model reconcile the fact that rural, older, and less college-educated cells have high spending intensities but typically lower MPCs?

MPCs and spending intensities are complementary but distinct determinants of dynamic multipliers at short horizons: high-MPC cells spend the transfer quickly (year 0–1), generating a large immediate impact, while high-spending-intensity cells ensure that spending, whenever it occurs, circulates domestically and reaches slack labor markets. At long horizons (year 4+) the two effects converge because all cells eventually spend their full transfer (long-run MPC = 1) and the multiplier converges to the static model’s value, which depends only on spending intensity. The practical implication is that policies targeting rural/older/less-educated cells (high intensity, lower MPC) may have lower immediate multipliers than policies targeting high-MPC urban consumers, but converge to higher long-run multipliers. The year-4 cumulative multipliers from the dynamic model closely resemble the static model, suggesting a 3–5 year business cycle horizon is well captured by the static analysis.

Q7. What does the triangular flow pattern imply for understanding regional inequality and fiscal redistribution?

The triangular flow — rural regions receive net income from foreign exports; rural consumers spend net inflows toward urban regions; urban consumers spend net toward abroad — means that rural regions’ incomes depend on export competitiveness while urban regions’ incomes depend on domestic consumption demand; fiscal transfers to rural consumers thus have high domestic multipliers because their spending boosts urban income (via the rural-to-urban spending flow), which then circulates domestically before leaking abroad. This pattern is also consistent with the political economy finding that high-multiplier cells (rural, older, less educated) are more likely to vote for right-wing populists and feel politically disenfranchised — they are the “left behind” groups that economic research associates with exposure to globalization and automation, but whose spending patterns happen to generate large domestic multipliers during recessions.

Key concepts

disaggregated economic accounts : a system that breaks down all national accounting positions — consumer spending, labor and profit income, intermediates trade, government transactions, foreign trade — into bilateral flows between consistently defined region-by-industry consumer cells and producer cells, satisfying national accounting identities both at the cell level and in aggregate; the paper’s proof of concept is implemented for Denmark using 2,744 consumer cells and 2,646 producer cells in 2018.

spending intensity : a cell-level, reduced-form statistic capturing how much a consumer cell’s spending contributes to the income of a target group of cells (e.g., all domestic cells or all slack cells), accounting for all indirect higher-order connections through the circular flow; formally defined as a recursive sum that incorporates the full disaggregated network structure; ranges from 0.4 to 0.9 for domestic spending intensity and is systematically higher for rural, older, and less college-educated cells.

slack cell : in the paper’s NK model, a consumer cell for which demand-driven unemployment occurs because the nominal wage rigidity binds — labor supply exceeds demand when the cell’s income declines by more than a threshold δ due to a negative demand shock; fiscal transfers with high multipliers are those whose spending reaches slack cells (directly or through higher-order network connections).

triangular flows : the cross-regional spending pattern documented for Denmark in which net consumption spending flows from rural regions to urban regions (urban bias), net foreign export revenue flows to rural regions (rural manufacturing), and net foreign import spending flows from urban regions; implies that rural-to-urban spending flows act as an important transmission channel for fiscal stimulus targeted at rural consumers.

bottom-up vs top-down disaggregation : the two methodological approaches for constructing bilateral cell-to-cell flows; the bottom-up approach uses individual-level microdata (e.g., bank transaction records) to directly observe cell-to-cell payment flows; the top-down approach allocates an aggregate national accounting position across cells using assignment algorithms informed by microdata; both approaches are designed so that the resulting disaggregated flows sum to the corresponding SNA aggregate.

The Macroeconomic Impact of Climate Change: Global Versus Local Temperature

Mon, 01 Jan 0001 00:00:00 +0000

The paper shows that the macroeconomic impact of climate change is an order of magnitude larger than what standard country-level panel estimates suggest. The key identification innovation is to measure the effect of global mean temperature shocks using time-series local projections, rather than using cross-country variation in local temperatures as in the conventional panel literature. A shock to global mean temperature tracks extreme weather events (droughts, heat waves, wind, precipitation anomalies) that affect all countries simultaneously; a local temperature anomaly in one country does not.

Empirical approach: The authors estimate local projections of world GDP growth on exogenous global mean temperature shocks. The shock is the innovation to global mean temperature after removing a 2-year autoregressive component and a low-frequency trend, following Hamilton (2018). Two estimation samples: BU (Barro-Ursúa macro history, 43 countries, 1860–2019) and PWT (Penn World Tables, 173 countries, 1960–2019).

Key empirical results (Section 3): A 1°C shock to global mean temperature causes world GDP to fall by 14% after 6 years in the PWT sample (95% CI: 6%–22%); significant at the 5% level in years 2–8; does not mean-revert within the 10-year sample horizon. In the BU sample, the peak GDP decline is 18% after 5 years (95% CI: 6%–30%). Converting the cumulative IRF ratio to a permanent temperature change yields a 22–34% long-run GDP decline per 1°C of permanent global warming (PWT and BU respectively). By contrast, local temperature shocks — estimated from a standard cross-country panel with country and year fixed effects — generate effects of 1–3% per °C, not statistically significant at the 5% level.

Why global > local (Section 4): Four categories of extreme climatic events (heat waves, droughts, wind, precipitation anomalies) jointly account for roughly half of the estimated global temperature effect on GDP. None of these are strongly correlated with local temperature anomalies because extreme weather reflects ocean-atmosphere dynamics (El Niño/ENSO) that elevate global mean temperature rather than any single country’s local temperature. In addition, capital and investment both decline persistently after global temperature shocks (capital response significant at 5% level), and warm/low-income countries are disproportionately affected.

Structural model (Section 5): A parsimonious neoclassical growth model embeds climate change as aggregate TFP changes. Households maximize ∫e^{−ρt}U(C_t)dt; firms use Cobb-Douglas technology Z_t K_t^α L_t^{1−α}. The damage function governing TFP is:

Z_t = Z_0 exp( ∫0^t ζ_s T̂{t−s} ds )

where T̂_t is excess global mean temperature above baseline and ζ_s = A(e^{−Bs} − e^{−Cs}) is the structural damage function. When ζ_s → 0, shocks have level but not growth effects; no statistically significant evidence of growth effects is found in Figure 3 of the paper. The model is calibrated with: risk aversion γ = 1 (log utility), capital share α = 0.33, annual capital depreciation δ = 0.08, and pure time preference ρ = 0.02. Proposition 1 (model inversion) shows that, to first order, ŷ_t = ẑ_t + α ∫K_{t,s} ẑ_s ds, where K_{t,s} is the sequence-space Jacobian of the neoclassical growth model. This delivers identification: observed output impulse responses recover the structural TFP damage function ζ_s without imposing functional form on the capital channel.

Estimation results (Section 5.3, Figure 12): The estimated damage function implies a 4% peak short-run productivity decline 2 years after a 1°C transitory global temperature shock; the effect decays slowly and remains significant for up to 10 years. The capital response (non-targeted moment) closely matches its empirical counterpart, providing an overidentification check. The local temperature damage function, estimated by targeting the local-panel output IRF, peaks at only 0.5% and is more than 8× smaller in cumulative productivity effect; it is not statistically different from zero at the 5% level.

Business-as-usual counterfactual (Section 6.1–6.2): Temperature rises from 2024, reaching 3°C above preindustrial by 2100 (asymptoting to 3.3°C), equivalent to 2°C of additional warming since 2024. Under the global temperature damage function:

World output by 2050: −28% vs. no-warming baseline
World output by 2100: −53% (accumulated TFP losses reach −40%)
Capital by 2100: −51% (investment initially rises as households anticipate lower permanent income, then decumulates rapidly)
Consumption by 2100: −53%
2024 welfare loss (consumption equivalent): 35%; welfare continues declining as temperatures rise, eventually reaching 56%
95% CI for 2100 output loss: 29%–77%
All effects statistically significant at the 5% level

Under the local temperature damage function with the same warming scenario: long-run output declines only 9%, welfare loss is 5%, and neither is statistically significant at the 5% or 10% level — consistent with conventional estimates (Nordhaus 1992, Dell et al. 2012, Burke et al. 2015).

Social Cost of Carbon (Section 6.2, Panel F): The SCC is defined as the consumption-equivalent amount households would pay at time 0 to avoid one additional ton of CO2, using the temperature-response function from Dietz et al. (2021a). Baseline result: $1,207 per ton (2024 international dollars), more than 6× larger than the $185/ton estimate in Rennert et al. (2022). 95% CI: $399–$2,015 per ton. Climate sensitivity range (half/double median): $600–$2,400 per ton. BU sample (larger damage functions): >$1,500 per ton. Using the local temperature damage function yields an SCC of only $149/ton, consistent with conventional estimates.

Sensitivity (Section 6.4): Higher time preference ρ > 0.04 lowers welfare losses below 20% and the SCC below 3× conventional high-end estimates — the only scenario where results converge toward prior estimates. Near-Stern discount rates (ρ → 0): welfare loss >40% and SCC >$2,500/ton. A 6°C-by-2100 scenario yields welfare losses >60%.

Historical growth accounting (Section 6.3): Starting the model in 1960 and imposing the realized 1960–2019 warming path, then holding temperature constant at its 2019 level, reveals:

World GDP per capita would be 25% higher today without warming since 1960
By 2040, output is 32% below potential from past warming — one-quarter of losses from historical warming are yet to materialize (due to delayed damage function and transitional capital dynamics)
Climate change reduced the annual world growth rate by as much as a third of baseline by the 21st century

Policy implication: Most decarbonization interventions cost ~$80/ton on average (Bistline et al. 2023). Under conventional SCC estimates based on local temperature ($149/ton), the US Domestic Climate Cost (DCC) falls below policy cost, making unilateral emissions reduction prohibitively expensive. Under the paper’s global temperature SCC of $1,207/ton, the DCC of the United States exceeds $80/ton even accounting for the fraction of global climate benefits that accrue domestically — unilateral decarbonization becomes cost-effective for large economies such as the US.

Scope conditions: The neoclassical model abstracts from adaptation, mitigation, trade, urbanization, and endogenous emissions. The identification assumption requires that global mean temperature innovations are uncorrelated with other global economic confounders at business-cycle and trend frequencies; the paper checks robustness against alternative detrending, exclusion of WWII and COVID-19 years, El Niño/ENSO controls, and instrumental variables for temperature based on solar/volcanic forcing. The conversion from medium-run to long-run effects relies on the constrained ζ_s = A(e^{−Bs} − e^{−Cs}) functional form ruling out growth effects — consistent with the data but not formally testable beyond the 10-year horizon. Counterfactuals involve 2–3°C temperature changes substantially beyond the sample’s moderate perturbations; the model’s extrapolation may understate damages if nonlinearities exist at extreme temperatures (the authors note their conservative constrained-form approach).

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. Why do global temperature shocks produce GDP effects an order of magnitude larger than local temperature panel estimates?

Global mean temperature shocks are strongly correlated with extreme weather events — heat waves, droughts, wind storms, and precipitation anomalies — that simultaneously affect all countries; these four event categories jointly account for roughly half of the global temperature effect on GDP. Local temperature anomalies in a given country (as measured in standard cross-country panels with year fixed effects absorbed) are not correlated with these same events, because El Niño/ENSO and related ocean-atmosphere dynamics elevate global mean temperature without proportionally elevating any one country’s local temperature. Local panel studies also implicitly allow economic activity to shift toward cooler regions within a given year — an option unavailable when global warming affects all locations simultaneously. The resulting bias in local-panel estimates is not “aggregation bias” in the sense of Jensen’s inequality, but rather an identification problem: local panels identify a different object (the effect of temperature relative to other countries in the same year) rather than the aggregate climate impact the paper measures.

Q2. What is the identification strategy and what are the main threats?

The global temperature shock is identified as the innovation to global mean temperature after removing a 2-year AR component and a Hamilton (2018) low-frequency trend, yielding a shock orthogonal to its own recent history and to long-run trends. The main threats are: (i) global business-cycle confounders (worldwide recessions that simultaneously lower activity and emissions), addressed by controlling for quadratic time trends and global aggregate demand proxies; (ii) reverse causality (economic expansion warming the atmosphere), addressed by IV estimates using solar/volcanic forcing as instruments; (iii) low-frequency correlation between climate trends and productivity growth, addressed by flexible detrending and robustness to sample period. All major specification checks generate quantitatively similar results, and the paper passes placebo tests for large global confounders (WWII, COVID-19).

Q3. How does the structural model translate medium-run shock responses into long-run warming effects?

Proposition 1 (model inversion) shows that the output impulse response decomposes into a direct TFP effect ẑ_t and a capital channel ŷ_t = ẑ_t + α ∫K_{t,s} ẑ_s ds, where K_{t,s} is the sequence-space Jacobian of the neoclassical growth model (Auclert et al. 2021); this allows recovery of the structural TFP damage function {ζ_s} from the observed 10-year output IRF by non-linear least squares, without having to observe TFP directly. The counterfactual for a gradually rising temperature path (BAU scenario with 2°C additional warming since 2024) is then solved via the full nonlinear model — not via the log-linearization used in estimation — because the 2–3°C excursion far exceeds the sample’s modest temperature perturbations. The capital response (non-targeted moment) closely tracks its empirical counterpart, providing a strong overidentification check that the model’s capital dynamics are correctly specified.

Q4. Why does capital initially rise in the BAU counterfactual before declining?

Following standard permanent-income logic, when households learn at date 0 that global temperatures will rise and future TFP will fall, they temporarily increase saving and investment to accumulate buffer capital before the productivity decline materializes; this front-loads some capital accumulation in the early transition years (2024–2030s), briefly pushing capital above baseline, before the accumulated TFP losses overwhelm the saving motive and capital begins an extended decline. The net effect is still a 51% capital shortfall by 2100 because persistently lower TFP reduces the marginal product of capital over decades, depressing investment and allowing the capital stock to drift far below its no-warming balanced growth path.

The SCC is defined as the dollar amount C such that households are indifferent between (a) a world where one additional ton of CO2 is emitted at time 0 and (b) a world in steady-state where the household has paid C at time 0 (equation 7: V^{ss}(K^{ss} − C) = V^{SCC}_0(K^{ss})). The temperature response to a 1-ton CO2 pulse is taken from Dietz et al. (2021a) — temperature peaks at 0.002°C after a 1-gigaton pulse and stabilizes. The model generates the productivity path {Z^{SCC}_t} via the structural damage function, solves for equilibrium capital and consumption paths, and computes the value function V^{SCC}_0. The resulting $1,207/ton exceeds prior estimates by 6× because the global-temperature damage function implies 4% peak TFP losses per 1°C transitory shock, compared to the ~0.5% peak implied by local temperature — and the SCC is essentially the capitalized sum of these future productivity losses, so the ratio scales proportionally.

Q6. Why are historical climate losses so large if year-to-year warming is small?

The key is cumulation: annual warming increments are individually small (tenths of a degree), but the damage function {ζ_s} is persistent (effects last 10+ years), so each year’s increment adds a flow of persistent TFP losses that stack on top of prior increments. The paper’s growth accounting shows that climate change reduced the world growth rate by up to one-third of baseline in the 21st century — a number that appears modest in any single year but, compounded over decades, translates into a 25% GDP per capita shortfall by 2019. Additionally, because the estimated damage function has a 2-year lag before peak TFP impact, a substantial share of past warming’s losses are yet to be realized — the paper estimates GDP will be 32% below its potential by 2040 even with no further warming.

Q7. What does the sensitivity analysis reveal about the robustness of the results?

The key sensitivity is the rate of time preference ρ: at ρ = 0.02 (baseline, consistent with secular interest rate decline), welfare loss is 35%; at ρ = 0.04 (above recent market rates), welfare loss is still above 20%; only at implausibly high discount rates does the welfare loss fall below 15%. The SCC is more sensitive to ρ than welfare because the SCC is a capitalized stock valuation while welfare is an annualized flow. BU sample damage functions (larger IRF) raise welfare loss to 42% and 2100 GDP loss to 61%; these represent the high end of the estimates. The climate sensitivity range ($600–$2,400/ton for the SCC) reflects uncertainty in the physics of CO2-to-temperature conversion, not in the estimated economic damage function. Across all these dimensions, the global-temperature estimates remain order-of-magnitude larger than local-temperature estimates.

Q8. What is the policy implication for large economies considering unilateral decarbonization?

The domestic decarbonization test compares the Domestic Climate Cost (DCC) — the fraction of the global SCC that accrues to the decarbonizing country — against the marginal cost of abatement (~$80/ton average, Bistline et al. 2023). Under conventional local-temperature estimates ($149/ton global SCC), the US DCC falls below $80/ton, implying unilateral action destroys domestic value. Under the paper’s $1,207/ton global SCC, the US DCC comfortably exceeds $80/ton even if the US only captures a fraction of world welfare gains — because global temperature extremes (hurricanes, heat waves, droughts) strike the US directly, the DCC/SCC ratio is much higher than under local estimates where the US appears less exposed. This fundamentally changes the cost-benefit calculus for large-economy unilateral climate policy.

Key concepts

global mean temperature shock: a time-series innovation to world average surface temperature, identified by Hamilton (2018) detrending; captures ocean-atmosphere climate variability (El Niño/ENSO) correlated with extreme weather events affecting all countries simultaneously; the paper’s key identification variable, distinct from local temperature variation used in standard cross-country panels.

global vs. local temperature effect: the paper’s central finding that the GDP effect per 1°C global mean temperature shock (14–18%) is an order of magnitude larger than the effect per 1°C local temperature shock (1–3%); the gap is explained by extreme climatic events (heat waves, droughts, wind, precipitation) that co-move with global mean temperature but not with individual countries’ local temperatures.

structural damage function (ζ_s): the kernel relating excess global mean temperature T̂_{t−s} to log TFP at time t, specified as ζ_s = A(e^{−Bs} − e^{−Cs}); estimated from the PWT output impulse response via model inversion (Proposition 1); implies a 4% peak TFP loss 2 years after a 1°C transitory shock, decaying slowly over 10 years; rules out permanent growth effects consistent with the data.

Social Cost of Carbon (SCC): the one-time dollar amount households would pay at time 0 to avoid one additional ton of CO2; equals (in the linear limit) the present discounted value of all flow consumption-equivalent welfare losses from the induced warming; paper estimates $1,207/ton (2024 international dollars), more than 6× prior estimates, because the global-temperature damage function implies much larger per-degree productivity losses than local-temperature estimates.

committed climate losses: future GDP shortfalls already locked in by past warming, arising because the estimated damage function has a delayed peak (year 2) and slow decay (10+ years) — temperature rises in recent years continue reducing productivity for the following decade; the paper estimates these committed losses alone will lower GDP 32% below potential by 2040 even with temperature held constant at 2019 levels.

BAU scenario: the business-as-usual warming path used for the main counterfactual — global mean temperature reaches 3°C above preindustrial by 2100 (asymptoting to 3.3°C), implying 2°C of additional warming since the 2024 baseline; under this scenario the model implies 53% GDP loss, 51% capital loss, 53% consumption loss, and a 35% consumption-equivalent welfare loss by 2100.

E01 | Macro Paper Warehouse

Disaggregated Economic Accounts

In depth

Q1. What is missing from standard national accounts that this paper’s system provides?

Q2. Why do rural, older, and less college-educated consumer cells have higher fiscal multipliers during an economy-wide recession?

Q3. Why does targeting directly exposed or slack cells not guarantee a high transfer multiplier after the U.S. tariff shock?

Q4. How does the paper ensure that the disaggregated flows satisfy national accounting identities?

Q5. What is the relationship between spending intensity and the standard fiscal multiplier formula?

Q6. How does the dynamic model reconcile the fact that rural, older, and less college-educated cells have high spending intensities but typically lower MPCs?

Q7. What does the triangular flow pattern imply for understanding regional inequality and fiscal redistribution?

Key concepts

The Macroeconomic Impact of Climate Change: Global Versus Local Temperature

In depth

Q1. Why do global temperature shocks produce GDP effects an order of magnitude larger than local temperature panel estimates?

Q2. What is the identification strategy and what are the main threats?

Q3. How does the structural model translate medium-run shock responses into long-run warming effects?

Q4. Why does capital initially rise in the BAU counterfactual before declining?

Q5. How is the Social Cost of Carbon defined and computed?

Q6. Why are historical climate losses so large if year-to-year warming is small?

Q7. What does the sensitivity analysis reveal about the robustness of the results?

Q8. What is the policy implication for large economies considering unilateral decarbonization?

Key concepts